Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Kar, Abhishek"'
Autor:
Kheradmand, Shakiba, Rebain, Daniel, Sharma, Gopal, Sun, Weiwei, Tseng, Jeff, Isack, Hossam, Kar, Abhishek, Tagliasacchi, Andrea, Yi, Kwang Moo
While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a good initi
Externí odkaz:
http://arxiv.org/abs/2404.09591
Autor:
Banani, Mohamed El, Raj, Amit, Maninis, Kevis-Kokitsi, Kar, Abhishek, Li, Yuanzhen, Rubinstein, Michael, Sun, Deqing, Guibas, Leonidas, Johnson, Justin, Jampani, Varun
Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their training task, their intermediate representations are useful for other visu
Externí odkaz:
http://arxiv.org/abs/2404.08636
Autor:
Engelhardt, Andreas, Raj, Amit, Boss, Mark, Zhang, Yunzhi, Kar, Abhishek, Li, Yuanzhen, Sun, Deqing, Brualla, Ricardo Martin, Barron, Jonathan T., Lensch, Hendrik P. A., Jampani, Varun
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background. Inverse rendering of an object based on unconstrained image collections i
Externí odkaz:
http://arxiv.org/abs/2401.10171
Autor:
Weber, Ethan, Hołyński, Aleksander, Jampani, Varun, Saxena, Saurabh, Snavely, Noah, Kar, Abhishek, Kanazawa, Angjoo
We propose NeRFiller, an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a captured 3D scene or object are missing due to mesh reconstruction failur
Externí odkaz:
http://arxiv.org/abs/2312.04560
Autor:
Kheradmand, Shakiba, Rebain, Daniel, Sharma, Gopal, Isack, Hossam, Kar, Abhishek, Tagliasacchi, Andrea, Yi, Kwang Moo
We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular, it is often trained by uniformly sampling the training domain, or through handcrafted heuristics
Externí odkaz:
http://arxiv.org/abs/2312.00075
Autor:
Cheng, Zezhou, Esteves, Carlos, Jampani, Varun, Kar, Abhishek, Maji, Subhransu, Makadia, Ameesh
A critical obstacle preventing NeRF models from being deployed broadly in the wild is their reliance on accurate camera poses. Consequently, there is growing interest in extending NeRF models to jointly optimize camera poses and scene representation,
Externí odkaz:
http://arxiv.org/abs/2306.05410
Autor:
Saxena, Saurabh, Herrmann, Charles, Hur, Junhwa, Kar, Abhishek, Norouzi, Mohammad, Sun, Deqing, Fleet, David J.
Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and monocular depth, surprisingly, without task-specific architectures an
Externí odkaz:
http://arxiv.org/abs/2306.01923
Autor:
Hedlin, Eric, Sharma, Gopal, Mahajan, Shweta, Isack, Hossam, Kar, Abhishek, Tagliasacchi, Andrea, Yi, Kwang Moo
Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images. To generate such images, these models must understand the semantics of the objects they are asked to generate. In this work we show
Externí odkaz:
http://arxiv.org/abs/2305.15581
Autor:
Alzayer, Hadi, Abuolaim, Abdullah, Chan, Leung Chun, Yang, Yang, Lou, Ying Chen, Huang, Jia-Bin, Kar, Abhishek
Smartphone cameras today are increasingly approaching the versatility and quality of professional cameras through a combination of hardware and software advancements. However, fixed aperture remains a key limitation, preventing users from controlling
Externí odkaz:
http://arxiv.org/abs/2304.03285
Autor:
Gupta, Kamal, Jampani, Varun, Esteves, Carlos, Shrivastava, Abhinav, Makadia, Ameesh, Snavely, Noah, Kar, Abhishek
We present a method for joint alignment of sparse in-the-wild image collections of an object category. Most prior works assume either ground-truth keypoint annotations or a large dataset of images of a single object category. However, neither of the
Externí odkaz:
http://arxiv.org/abs/2303.16201